{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:04:41Z","timestamp":1757617481185,"version":"3.44.0"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031813740"},{"type":"electronic","value":"9783031813757"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-81375-7_9","type":"book-chapter","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T03:15:54Z","timestamp":1739416554000},"page":"150-166","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Towards Fairness-Aware Predictive Process Monitoring: Evaluating Bias Mitigation Techniques"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8187-8541","authenticated-orcid":false,"given":"Mickaelle Caldeira","family":"da Silva","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6261-1497","authenticated-orcid":false,"given":"Marcelo","family":"Fantinato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3551-6480","authenticated-orcid":false,"given":"Sarajane Marques","family":"Peres","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,14]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining: Data Science in Action","author":"WMP van der Aalst","year":"2016","unstructured":"van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016)","edition":"2"},{"key":"9_CR2","series-title":"LNBIP","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-031-08848-3_1","volume-title":"Process Mining Handbook","author":"WMP van der Aalst","year":"2022","unstructured":"van der Aalst, W.M.P.: Process mining: a 360 degree overview. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 3\u201334. Springer, Cham (2022)"},{"key":"9_CR3","unstructured":"Bellamy, R.K.E., Dey, K., Hind, M., et al.: AI fairness 360 (AIF360) (2024). Repository. https:\/\/github.com\/Trusted-AI\/AIF360"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Bellamy, R.K., et al.: AI fairness 360: an extensible toolkit for detecting and mitigating algorithmic bias. IBM J. Res. Dev. 63(4\/5), 4-1 (2019)","DOI":"10.1147\/JRD.2019.2942287"},{"key":"9_CR5","unstructured":"Berti, A., van Zelst, S.J., van\u00a0der Aalst, W.M.P.: PM4Py web services: easy development, integration and deployment of process mining features in any application stack. In: Demonstration Track at BPM 2019, pp. 174\u2013178 (2019)"},{"issue":"1","key":"9_CR6","doi-asserted-by":"publisher","first-page":"4209","DOI":"10.1038\/s41598-022-07939-1","volume":"12","author":"A Castelnovo","year":"2022","unstructured":"Castelnovo, A., Crupi, R., Greco, G., Regoli, D., Penco, I.G., Cosentini, A.C.: A clarification of the nuances in the fairness metrics landscape. Sci. Rep. 12(1), 4209 (2022)","journal-title":"Sci. Rep."},{"issue":"5","key":"9_CR7","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1145\/3376898","volume":"63","author":"A Chouldechova","year":"2020","unstructured":"Chouldechova, A., Roth, A.: A snapshot of the frontiers of fairness in machine learning. Commun. ACM 63(5), 82\u201389 (2020)","journal-title":"Commun. ACM"},{"key":"9_CR8","series-title":"LNBIP","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1007\/978-3-031-08848-3_10","volume-title":"Process Mining Handbook","author":"C Di Francescomarino","year":"2022","unstructured":"Di Francescomarino, C., Ghidini, C.: Predictive process monitoring. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 320\u2013346. Springer, Cham (2022)"},{"key":"9_CR9","unstructured":"Equal Employment Opportunity Commission: Uniform guidelines on employee selection procedures, USA (1978). https:\/\/www.govinfo.gov\/content\/pkg\/CFR-2011-title29-vol4\/xml\/CFR-2011-title29-vol4-part1607.xml"},{"issue":"7","key":"9_CR10","doi-asserted-by":"publisher","first-page":"1445","DOI":"10.1109\/TKDE.2012.72","volume":"25","author":"S Hajian","year":"2012","unstructured":"Hajian, S., Domingo-Ferrer, J.: A methodology for direct and indirect discrimination prevention in data mining. IEEE Trans. Knowl. Data Eng. 25(7), 1445\u20131459 (2012)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"9_CR11","unstructured":"Hardt, M., Price, E., Srebro, N.: Equality of opportunity in supervised learning. In: Annual Conference on Neural Information Processing Systems, pp. 3315\u20133323 (2016)"},{"issue":"1","key":"9_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10115-011-0463-8","volume":"33","author":"F Kamiran","year":"2012","unstructured":"Kamiran, F., Calders, T.: Data preprocessing techniques for classification without discrimination. Knowl. Inf. Syst. 33(1), 1\u201333 (2012)","journal-title":"Knowl. Inf. Syst."},{"key":"9_CR13","doi-asserted-by":"crossref","unstructured":"Liu, W., Liu, F., Tang, R., Liao, B., Chen, G., Heng, P.A.: Balancing between accuracy and fairness for interactive recommendation with reinforcement learning. In: 24th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 155\u2013167. Springer (2020)","DOI":"10.1007\/978-3-030-47426-3_13"},{"key":"9_CR14","doi-asserted-by":"crossref","unstructured":"Maggi, F.M., Di\u00a0Francescomarino, C., Dumas, M., Ghidini, C.: Predictive monitoring of business processes. In: 26th International Conference on Advanced Information Systems Engineering, pp. 457\u2013472. Springer (2014)","DOI":"10.1007\/978-3-319-07881-6_31"},{"key":"9_CR15","series-title":"LNBIP","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/978-3-031-08848-3_12","volume-title":"Process Mining Handbook","author":"F Mannhardt","year":"2022","unstructured":"Mannhardt, F.: Responsible process mining. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 373\u2013401. Springer, Cham (2022)"},{"issue":"6","key":"9_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3457607","volume":"54","author":"N Mehrabi","year":"2021","unstructured":"Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., Galstyan, A.: A survey on bias and fairness in machine learning. ACM Comput. Surv. 54(6), 1\u201335 (2021)","journal-title":"ACM Comput. Surv."},{"key":"9_CR17","unstructured":"Pleiss, G., Raghavan, M., Wu, F., Kleinberg, J., Weinberger, K.Q.: On fairness and calibration. In: Annual Conference on Neural Information Processing Systems, pp. 5680\u20135689. Springer (2017)"},{"key":"9_CR18","doi-asserted-by":"publisher","unstructured":"Pohl, T., Berti, A.: (Un)fair process mining event logs (2023). Dataset. https:\/\/doi.org\/10.5281\/zenodo.8059488","DOI":"10.5281\/zenodo.8059488"},{"key":"9_CR19","unstructured":"Pohl, T., Berti, A., Qafari, M.S., van\u00a0der Aalst, W.M.P.: A collection of simulated event logs for fairness assessment in process mining. In: Demonstration & Resources Forum at BPM 2023, pp. 87\u201391 (2023)"},{"key":"9_CR20","doi-asserted-by":"crossref","unstructured":"Qafari, M.S., van\u00a0der Aalst, W.M.P.: Fairness-aware process mining. In: International Conference on Cooperative Information Systems, pp. 182\u2013192. Springer (2019)","DOI":"10.1007\/978-3-030-33246-4_11"},{"issue":"5","key":"9_CR21","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1017\/S0269888913000039","volume":"29","author":"A Romei","year":"2014","unstructured":"Romei, A., Ruggieri, S.: A multidisciplinary survey on discrimination analysis. Knowl. Eng. Rev. 29(5), 582\u2013638 (2014)","journal-title":"Knowl. Eng. Rev."},{"key":"9_CR22","unstructured":"Saleiro, P., et al.: Aequitas: a bias and fairness audit toolkit. preprint arXiv:1811.05577 (2018)"},{"key":"9_CR23","doi-asserted-by":"crossref","unstructured":"de\u00a0Sousa, R.G., Peres, S.M., Fantinato, M., Reijers, H.A.: Concept drift detection and localization in process mining: an integrated and efficient approach enabled by trace clustering. In: 36th Annual ACM Symposium on Applied Computing, pp. 364\u2013373. ACM (2021)","DOI":"10.1145\/3412841.3441918"},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Weske, M.: Business Process Management: Concepts, Languages, Architectures, 3 edn. Springer (2019)","DOI":"10.1007\/978-3-662-59432-2_1"},{"key":"9_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, B.H., Lemoine, B., Mitchell, M.: Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 335\u2013340. ACM (2018)","DOI":"10.1145\/3278721.3278779"}],"container-title":["Lecture Notes in Computer Science","Cooperative Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-81375-7_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T05:01:06Z","timestamp":1757134866000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-81375-7_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031813740","9783031813757"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-81375-7_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"14 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CoopIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Cooperative Information Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"coopis2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/coopis.scitevents.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}